In this study, a new modeling method is proposed, in which the human abilities to extract information selectively corresponding to his or her point of view and to organize it adaptively for the purpose of problem solving is treated. And the computer algorithms executing the problem solving processes based on the model are developed. The main results obtained are as follows :A method is presented which extracts the primitive events corresponding to knowledge representation units. Concrete cause-effect sequences. that is, ' sinarios ' are formed in their sequences. Also, the inference method to organize the deep structures of problems involved in the concrete cause-effect sequences is presented based on the representation units.The process to create the complete and consistent knowledge from the incomplete and sometimes inconsistent information from the actual world is formulated, which is based on ' order relations ' goveming the world for instance, in an apartment, Keeping dogs are prohibited is a tacit understanding, if the case of, keeping cats are prohibited is true. ').A method to transform raw information such as signals or signs from the environment into the knowledge represented as symbols is proposed, in which the notion of ' constraint interval fuzzy sets ' is introduced based on ' interval constraints ' which represent the constraints goveming the concemed problem domain. Also, the same problem is treated with the method combining the deductive Teaming EBL (Explanation-based teaming and the data teaming methods such as the neural network and the genetic algorithm techniques.A method to create the global and consistent descriptions of the system states from the partial information is presented based on the decentralized cooperative problem solving scheme by distributed sensing and processing units. Also. the same problem is now continuing to formulate as the problem of ' synagetics which ia a theory of creating order in a living thing.